Current and Future Global Burden of Early-Onset Pancreatic Cancer: A Population-Based Study
Bibliographic record
Abstract
PURPOSE: Early-onset pancreatic cancer (EOPC) is an emerging global health concern, defined as pancreatic cancer (PC) diagnosed before age 50 years. This study aims to evaluate global trends in EOPC incidence, mortality, and associated risk factors, with projections of future burden through 2040. METHODS: A population-based analysis was conducted using the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 data set, encompassing data from 204 countries and territories. EOPC was defined as PC diagnosed in individuals age 15-49 years. Statistical analyses and visualizations were performed using R (version 4.3.2) and Python (version 3.11). RESULTS: Globally, EOPC incident cases increased 1.63-fold from 19,366 in 1990 to 31,531 in 2021. Mortality rose by 57% during the same period, with a total of 26,996 deaths in 2021. Age-standardized incidence and mortality rates declined slightly, reflecting demographic changes and population growth. Middle Sociodemographic Index (SDI) regions exhibited the most rapid increases in both cases and deaths. In 2021, smoking accounted for 8.1% of EOPC deaths, whereas high fasting plasma glucose and high BMI contributed to 6.3% and 5.7% of deaths, respectively. Projections indicate a 10.6% global increase in EOPC cases and deaths by 2040, with the largest absolute increases expected in low-middle SDI regions. CONCLUSION: EOPC incidence and mortality are rising globally, with middle and low-middle SDI regions facing the most rapid increases and projected to carry the greatest burden by 2040. These findings emphasize the need for primary prevention, access to diagnostics, and equitable health care systems. Addressing modifiable risk factors, such as smoking and metabolic syndrome, will be critical in reducing the overall impact of EOPC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".